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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Model reduction and dynamic matrices extraction from state-space representation applied to rotating machines

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Author(s):
Saint Martin, Leonardo B. [1] ; Mendes, Ricardo U. [1] ; Cavalca, Katia L. [1]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Fac Mech Engn, Dept Integrated Syst, 200 Mendeleyev St, BR-13083860 Campinas, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: MECHANISM AND MACHINE THEORY; v. 149, JUL 2020.
Web of Science Citations: 0
Abstract

Model reduction is a relevant subject within the field of rotordynamics since low order models are fundamental for control strategies design and implementation, health monitoring, behavior prediction, Fault Detection and Identification (FDI) and stochastic analyses. In this context, this article proposes a complete review of three widely used reduction methods: static or Guyan technique, the System Equivalent Reduction Expansion Process (SEREP) and the modified SEREP. Regarding SEREP, a new approach is presented in which right and left eigenvectors from the undamped original system (with mass and stiffness matrices not symmetric) are used to transform all original system dynamic matrices. To modified SEREP (that contemplates all original system characteristics, including frequency dependent damping and gyroscopic effect) an extraction from the reduced state-space representation is achieved to build rotor and bearings reduced dynamic matrices with physical interpretability. A set of practical recommendations is presented, highlighting key aspects to increase reduction success chances. The methods are applied to two different rotors and results show satisfactory agreement between reduced and complete model responses when analyzing Frequency Response Functions (FRFs) and Campbell diagrams. The computational costs of processing each reduced model and running common rotordynamic analyses with reduced and complete models are compared. (C) 2020 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 15/20363-6 - Fault tolerant identification and control of rotating systems
Grantee:Katia Lucchesi Cavalca Dedini
Support Opportunities: Research Projects - Thematic Grants